Paper Citation Record · LEDGER

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation

As of 16 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2412.02262.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.02262 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:41:41.515573Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:08:58.050673Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T21:08:58.212497Z

Reference resolution

31 of 31 outbound references displayed

verified exact8
verified fuzzy9
unresolved13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 29b320c0-d0ae-48dd-b7d2-af8f30e2480c · outbound

This paper cites Wild salmon enumeration and monitoring using deep learning empow- ered detection and tracking.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Wild salmon enumeration and monitoring using deep learning empow- ered detection and tracking

Reference 1

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Observation 5e6bbc10-6c55-4375-a10d-d356830a6be0 · outbound

This paper cites Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs

Reference 2

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Observation 8bb92a7c-af3d-405c-ad44-385ac67d098b · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recogni- tion at Scale.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation An Image is Worth 16x16 Words: Transformers for Image Recogni- tion at Scale

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:42.791378Z

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Observation d48afad5-34ad-4bda-99b6-4f6c30e29f1a · outbound

This paper cites Knowledge Augmented Instruction Tuning for Zero-shot Animal Species Recognition.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Knowledge Augmented Instruction Tuning for Zero-shot Animal Species Recognition

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:42.767809Z

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Observation 67d7ca62-2d10-4680-9ecc-073068561787 · outbound

This paper cites Joint SDG Fund | Goal 14: Life below water.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Joint SDG Fund | Goal 14: Life below water

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:42.747194Z

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Observation aeee8331-6626-4a1d-8d8f-8795f07f70cd · outbound

This paper cites REALM: Retrieval-Augmented Language Model Pre-Training.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation REALM: Retrieval-Augmented Language Model Pre-Training

Reference 6

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Source-reported events for the cited work

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Observation d30c9162-2cd7-4f31-922f-374e61a06702 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Deep Residual Learning for Image Recognition

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T23:41:41.354741Z

Source-reported events for the cited work

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Observation f0e327bb-4f50-41b1-966a-f9be952974be · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T23:41:41.362035Z

Source-reported events for the cited work

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Observation f84ed92c-1b3a-4a48-bbb4-cb680dde8698 · outbound

This paper cites REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory

Reference 9

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Observation ebef789a-774d-4c7b-9bdb-8ac6685034a8 · outbound

This paper cites Active Retrieval Augmented Generation.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Active Retrieval Augmented Generation

Reference 10

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Source-reported events for the cited work

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Observation 02567d19-32f8-421f-8c00-144407b67629 · outbound

This paper cites FathomNet: A global image database for enabling artificial intelligence in the ocean.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation FathomNet: A global image database for enabling artificial intelligence in the ocean

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:42.727632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fc83bae2-e79c-479d-b7e2-898892a1590c · outbound

This paper cites The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T23:41:41.396405Z

Source-reported events for the cited work

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Observation 539593de-eb52-497f-b5b7-a67952e93b5b · outbound

This paper cites ImageNet Classification with Deep Convolutional Neural Networks.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation ImageNet Classification with Deep Convolutional Neural Networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:42.708274Z

Source-reported events for the cited work

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Observation a72b9a4c-ce03-451d-b5d1-16ecc87e5e16 · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T23:41:41.411781Z

Source-reported events for the cited work

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Observation ac06c90b-220c-4922-bf3e-5aef7eb10c32 · outbound

This paper cites Grounded Language-Image Pre-training.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Grounded Language-Image Pre-training

Reference 15

Resolution: verified exact; raw_fallback, observed 2026-08-11T23:41:42.361860Z

Source-reported events for the cited work

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Observation 0076c009-5bf5-4e80-a9b4-351baf72c9a3 · outbound

This paper cites Visual Instruction Tuning.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Visual Instruction Tuning

Reference 16

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Source-reported events for the cited work

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Observation af48f09c-8a81-4ec3-a30d-b8f6c070f987 · outbound

This paper cites KRISP: Integrating Implicit and Symbolic Knowledge for Open- Domain Knowledge-Based VQA.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation KRISP: Integrating Implicit and Symbolic Knowledge for Open- Domain Knowledge-Based VQA

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-11T23:41:42.257825Z

Source-reported events for the cited work

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Observation b7cc8d75-a0ac-4c2b-a3a5-21f9578f77a9 · outbound

This paper cites OK-VQA: A Visual Question Answering Benchmark Requiring Exter- nal Knowledge.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation OK-VQA: A Visual Question Answering Benchmark Requiring Exter- nal Knowledge

Reference 18

Resolution: verified exact; raw_fallback, observed 2026-08-11T23:41:42.171880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3c2bd38b-8bee-4502-87b0-2599141d6b49 · outbound

This paper cites New frontiers in AI for biodiversity research and conservation with multimodal language models.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation New frontiers in AI for biodiversity research and conservation with multimodal language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:42.689102Z

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Observation 5358996d-cb21-480c-a9a1-87b151644736 · outbound

This paper cites A deep active learning system for species identification and counting in camera trap images.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation A deep active learning system for species identification and counting in camera trap images

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:41:42.070193Z

Source-reported events for the cited work

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Observation ffb93696-6913-4a47-8ed8-a1dae51f9936 · outbound

This paper cites Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Reference 21

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Observation 7a972156-5d2a-478c-9caf-1dd21b047f55 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervi- sion.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Learning Transferable Visual Models From Natural Language Supervi- sion

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:42.668802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5ee30694-4b65-4bcf-b469-e2cc1dbe3124 · outbound

This paper cites SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentation.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentation

Reference 23

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Source-reported events for the cited work

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Observation 4e4456c4-02a9-4c53-8876-89ce403ecf11 · outbound

This paper cites You Only Look Once: Unified, Real-Time Object Detection.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation You Only Look Once: Unified, Real-Time Object Detection

Reference 24

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Source-reported events for the cited work

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Observation 444c47a0-07fa-433b-877c-f53d199ab57c · outbound

This paper cites Retrieval-Augmented Transformer for Image Captioning.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Retrieval-Augmented Transformer for Image Captioning

Reference 25

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Observation 67858487-0c8c-4611-af1d-7a4d38a671b3 · outbound

This paper cites K-LITE: Learning Transferable Visual Models with External Knowl- edge.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation K-LITE: Learning Transferable Visual Models with External Knowl- edge

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:42.647250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f2b0088a-0b81-4756-829a-cacbfd8a1cb6 · outbound

This paper cites BioCLIP: A Vision Foundation Model for the Tree of Life.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation BioCLIP: A Vision Foundation Model for the Tree of Life

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:42.629866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b819b07f-d332-4c90-8e8d-9dbbf9d32e74 · outbound

This paper cites The iNaturalist Species Classification and Detection Dataset.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation The iNaturalist Species Classification and Detection Dataset

Reference 28

Resolution: verified exact; raw_fallback, observed 2026-08-11T23:41:41.895892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9185048f-4e26-452c-b128-e2fcc4df740f · outbound

This paper cites Advancing artificial intelligence in fisheries requires novel cross-sector collaborations.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Advancing artificial intelligence in fisheries requires novel cross-sector collaborations

Reference 29

Resolution: verified exact; doi, observed 2026-08-11T23:41:41.575579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 430791f0-3760-4813-a2f7-4a52502f63b4 · outbound

This paper cites Multi-Modal Answer Validation for Knowledge-Based VQA.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation Multi-Modal Answer Validation for Knowledge-Based VQA

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:41:41.800943Z

Source-reported events for the cited work

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Observation a537c598-4f1f-48b1-9fd1-d6447bc5d05f · outbound

This paper cites MSR-VTT: A Large Video Description Dataset for Bridging Video and Lan- guage.

Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation MSR-VTT: A Large Video Description Dataset for Bridging Video and Lan- guage

Reference 31

Resolution: verified exact; raw_fallback, observed 2026-08-11T23:41:41.774782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation c067fa4c-c462-4a07-b63b-423631a37309 · inbound

Environmental large language model Evaluation (ELLE) dataset: A Benchmark for Evaluating Generative AI applications in Eco-environment Domain cites this paper.

Environmental large language model Evaluation (ELLE) dataset: A Benchmark for Evaluating Generative AI applications in Eco-environment Domain Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T21:08:58.219723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.